Record link: 
http://hdl.handle.net/20.500.12708/223007
-
Title: 
Uncertainty and error quantification in machine learning potentials
en
Citation: 
Heid, E. (2025). Uncertainty and error quantification in machine learning potentials. In Uncertainty quantification in atomistic  modeling: From uncertainty-aware  density functional theory to machine  learning : November 25 - 28, 2025 : CECAM-HQ-EPFL, Lausanne, Switzerland (pp. 15–15).
-
Publication Type: 
Inproceedings - Abstract Book Contribution
en
Language: 
English
-
Authors: 
Heid, Esther 
-
Organisational Unit: 
E165-03-1 - Forschungsgruppe Theoretische Materialchemie 
E056-23 - Fachbereich Innovative Combinations and Applications of AI and ML (iCAIML) 
-
Published in: 
Uncertainty quantification in atomistic modeling: From uncertainty-aware density functional theory to machine learning : November 25 - 28, 2025 : CECAM-HQ-EPFL, Lausanne, Switzerland 
-
Date (published): 
26-Nov-2025
-
Event name: 
Uncertainty quantification in atomistic modeling: From uncertainty-aware density functional theory to machine learning 
en
Event date: 
25-Nov-2025 - 28-Nov-2025
-
Event place: 
Lausanne, Switzerland
-
Number of Pages: 
1
-
Keywords: 
graph models; molecular AI; reaction AI
en
Research Areas: 
Modeling and Simulation: 50%
Computational Materials Science: 50%
-
Science Branch: 
1040 - Chemie: 100%
-
Appears in Collections:
Conference Paper

Show full item record

Google ScholarTM

Check